Attracting investor attention through advertising

By Dong Lou. 2014

403 citations recorded by the source corpus.

Subfields: Accounting, Finance

View Paper: https://www.semanticscholar.org/paper/5c3f61bee45c3b79194af780649d00cc0d1270d2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Accounting

1. Chen Xue
2. Kewei Hou
3. Lu Zhang
4. Ronnie Sadka
5. Gideon Ozik
6. Siyi Shen
7. Zhi Da
8. Azi Ben-Rephael

In Finance

1. Chen Xue
2. Kewei Hou
3. Lu Zhang
4. Lasse Heje Pedersen
5. Zhi Da
6. Ronnie Sadka
7. Gideon Ozik
8. Siyi Shen

Citations in the corpus

• Textual analysis in finance (2019, methodology): “To build the economic uncertainty index, we consider two groups of words: (a) the uncertainty category of Loughran-McDonald master dictionary developed in Loughran and McDonald (2011) translated to Portuguese, which we call V U ; and (b) a group containing the words {economia, econômico}, which is t”
• Flattening the Illiquidity Curve: Retail Trading During the COVID-19 Lockdown (2020, background): “Given the evidence in Barber and Odean (2008) that retail investors tend to trade attention-grabbing stocks, we also focus on stocks mentioned by the media, specifically in the context of COVID-19.”
• Game On: Social Networks and Markets (2021, background): “Firms use their communication and advertising to try to boost their stock prices (Lou (2014)).”
• It Depends on Where You Search: Institutional Investor Attention and Underreaction to News (2017, methodology): “To filter the data in order to exclude mass automated hits and mistakes, we follow the procedure used in Loughran and McDonald (2015) and our results are robust to using the filters described in deHaan, Shevlin, and Thornock (2015). Specifically, we exclude hits flagged as webcrawlers and exclude IP”
• Peer effects in corporate disclosure decisions (2020, positive): “…with this idea, prior research demonstrates that investors are attention-constrained and that firms use voluntary disclosures to attract investor attention and keep investors informed about the firm (Cohen and Frazzini, 2008; Barber and Odean, 2008; Engelberg and Parsons, 2011; Lou, 2014).”
• Attention allocation and return co-movement: Evidence from repeated natural experiments (2019, background): “2 Proxies include Google searches (Da, Engelberg, and Gao, 2011; Liu and Peng, 2015), extreme returns (Barber and Odean, 2008), trading volume (Barber and Odean, 2008; Gervais, Kaniel, and Mingelgrin, 2001), and Hou, Peng, and Xiong, 2009), news and headlines (Barber and Odean, 2008; Fang and Peress”
• A Comparison of New Factor Models (2017, background): “Panel A: Momentum (57) Sue1 Earnings surprise (1-month holding period), Sue6 Earnings surprise (6-month holding period), Foster, Olsen, and Shevlin (1984) Foster, Olsen, and Shevlin (1984) Sue12 Earnings surprise Abr1 Cumulative abnormal stock returns (12-month holding period), around earnings annou”
• Investor Attention and Stock Returns (2020, neutral): “…high (Li andYu (2012)), analyst coverage (Hirshleifer and Teoh (2003), Peng (2005), and Hirshleifer et al. (2013)), changes in advertising expenses (Lou (2014)); media coverage (Barber and Odean (2008), Fang and Peress (2009)), mutual fund inflow and outflow, Google search volume (Da et al.…”